WPS5157
Policy Research Working Paper 5157
Corruption and Confidence
in Public Institutions
Evidence from a Global Survey
Bianca Clausen
Aart Kraay
Zsolt Nyiri
The World Bank
Development Research Group
Macroeconomics and Growth Team
December 2009
Policy Research Working Paper 5157
Abstract
Well-functioning institutions matter for economic institutions. This correlation is robust to the inclusion of
development. In order to operate effectively, public a large set of controls for country and respondent-level
institutions must also inspire confidence in those they characteristics, and they show how it can plausibly be
serve. The authors use data from the Gallup World interpreted as reflecting at least in part a causal effect
Poll, a unique and very large global household survey, from corruption to confidence. The authors also show
to document a quantitatively large and statistically that individuals with low confidence in institutions
significant negative correlation between corruption exhibit low levels of political participation, show
and confidence in public institutions. This suggests increased tolerance for violent means to achieve political
an important channel through which corruption can ends, and have a greater desire to "vote with their feet"
inhibit development by eroding confidence in public through emigration.
This paper--a product of the Macroeconomics and Growth Team, Development Research Group--is part of a larger effort
in the department to study the causes and consequences of governance for economic development. Policy Research Working
Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akraay@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Corruption and Confidence in Public Institutions:
Evidence from a Global Survey
Bianca Clausen (World Bank), Aart Kraay (World Bank), and Zsolt Nyiri (German
Marshall Fund)1
1
bclausen@worldbank.org, akraay@worldbank.org, znyiri@gmfus.org. We would like to thank the
Knowledge for Change (KCP) Program of the World Bank for financial support, and Claudio Raddatz,
Mary Hallward-Driemeier, and Alfonso Astudillo for helpful comments. We are also grateful to the Gallup
Organization for enabling this project by providing access to the Gallup World Poll data, and especially to
Gale Muller, Vice Chairman and General Manager of the Gallup World Poll, and to seminar participants at
Gallup. The views expressed here do not reflect those of the Gallup Organization, the World Bank, its
Executive Directors, or the countries they represent.
1. Introduction
Despite considerable debate over definitions, measurement, and methodology, it
is widely-accepted among academics and policymakers that well-functioning public
institutions play an important role in economic development. In turn, a key ingredient in
the effectiveness of public institutions is the confidence that they inspire among those
whom they serve. For example, households or firms who do not have confidence in the
police or the courts are unlikely to avail themselves of their services, and may resort to
less efficient means of property protection or dispute resolution. Similarly, if individuals
lack confidence in the honesty of the electoral process they are unlikely to vote, leading
to low turnout rates that cast doubt on elected officials popular mandates and their ability
to carry out their agendas.2
In this paper we empirically investigate the role of corruption in undermining
confidence in public institutions. We document a quantitatively large and statistically
significant partial correlation between measures of corruption and confidence in public
institutions using a unique dataset. The Gallup World Poll (GWP) is a new and very
large cross-country household survey, interviewing more than 100,000 households in
over 150 countries, annually or biennially in most countries since 2006. We use
questions from the 2008 wave of the GWP, covering over 78,000 respondents in 90
countries to study the links between corruption and confidence in public institutions in
both developed and developing countries. Not surprisingly, in countries where
respondents report a high incidence of personal experiences with corruption, and in
which perceived corruption is widespread, confidence in public institutions is also low.
Much more interestingly, we show that this pattern also holds across individuals within
countries: individuals who experience corruption and who report that corruption is
widespread also tend to have lower confidence in public institutions. We show that this
correlation is robust to the inclusion of a large set of variables to control for respondent-
level characteristics, including a number of proxies intended to capture the respondents
2
These effects of corruption on confidence have not been lost on policymakers. A recent quotation from
Kai Eide, UN Special Representative of the Secretary-General for Afghanistan, neatly encapsulates this
view, "..[Corruption] pushes people away from the state and undermines our joint efforts to build peace,
stability and progress for Afghanistan's peoples." UNAMA Press Release, United Nations Assistance
Mission, August 20, 2008.
2
tendency to complain and report more negatively on corruption and confidence than
might otherwise be objectively warranted.
We are not the first to empirically explore the links between corruption and
confidence in public institutions. Relative to the existing literature (which we discuss in
more detail below), we offer three important contributions. First and most basic, our
study covers a much larger set of countries and respondents than any previous work,
which due to data limitations typically has been focused on small, regionally-focused
samples of countries. Second, several features of the GWP allow us to include a very
rich set of respondent-level control variables, importantly including proxies for
respondents unobserved propensity to respond negatively to both questions about
corruption and confidence that might artificially bias our results towards finding a strong
effect of corruption on confidence.
Third and perhaps most important, we offer a serious analysis of an identification
problem that has largely been ignored by the existing literature. Simply documenting that
survey respondents answer "yes" to a question like "is corruption a problem in your
country" and "no" to a question like "are you confident in your national government", as
most of the previous literature has done, does little to identify the direction of causation
between the two. Perhaps respondents perceptions of the prevalence of corruption drive
their low confidence in institutions, but just as plausibly the opposite could be true:
individuals who lack confidence in public institutions might as a result express the view
that corruption is widespread. We are able to provide suggestive evidence on the extent
of the biases that this endogeneity problem might create by exploiting the difference in
responses to two questions asked in the GWP. As we discuss in more detail below, the
GWP asks both a generalized perceptions of corruption question, as well as a very
specific experiential question which asks whether the respondent has been asked for a
bribe in the past 12 months. The advantage of the latter question is that it is much more
plausibly exogenous to respondents confidence in public institutions since it in large part
reflects the decision of a public official to solicit a bribe from the respondent, rather than
the respondents own characteristics. Consistent with this view, we find that the
estimated effect of the experiential corruption question is substantially smaller and less
3
statistically significant than the corresponding estimated effect using the generalized
perceptions question. However it remains strongly significant and quantitatively large,
supporting our claim of an important and plausibly causal effect running from corruption
to confidence in public institutions.
The rest of this paper proceeds as follows. In the next section we review the
related literature. Section 3 contains our main empirical results linking corruption to
confidence in public institutions. In Section 4 we explore a number of robustness checks
for this partial correlation, and in Section 5 we discuss in detail the identification problem
and potential solutions. In Section 6 we briefly document the consequences of the
corruption-induced loss of confidence in public institutions, showing that individuals
with low confidence in public institutions are less likely to engage in the political
process, are more likely to condone violence as a means to further political ends, and are
more likely to "vote with their feet" by emigrating. Section 7 concludes.
2. Related Literature
It is widely accepted by scholars and policymakers that well-functioning
institutions are important for development. This conviction has been informed by a wide
range of historical analysis, case studies, and cross-country empirical analysis. A few
examples from this very large literature include North (1990), Knack and Keefer (1995,
1997), Kaufmann et al. (2000), Acemoglu et al. (2001), and Rodrik et al. (2004). The idea
that a lack of confidence in public institutions undermines their effectiveness has also
been widely studied. A few examples of this literature include Easton (1965, 1975),
Gibson and Caldeira (1995), Putnam (2000), Uslaner (2002), Gibson et al. (2003), and
Mishler and Rose (2005). There is also a large literature on the direct economic
consequences of corruption for growth and investment, including Mauro (1995), Knack
and Keefer (1995), Mo (2001), Pellegrini and Gerlagh (2004), and M. on and Sekkat
(2005), and reviewed by Meon and Sekkat (2004) and Lambsdorff (2007).
Our contribution is to the small but growing literature on the effects of corruption
that operate through confidence in public institutions, which we discuss in somewhat
greater detail. A number of early papers in this literature exploit essentially country-level
variation in perceptions of corruption and confidence in public institutions. These
4
include Pharr (2000) who looks at aggregate data over time for one country (Japan);
Della Porta (2000) who provides a verbal discussion of country-level averages of both
corruption and confidence for just three countries; and Anderson and Tverdova (2003)
who combine country-level data on corruption perceptions from the Transparency
International Corruption Perceptions Index with household survey data on confidence
from 16 mostly developed countries. The major drawback of such studies is that they
cannot control for excluded country characteristics (or year effects in the case of Pharr
(2000)) that very plausibly might confound the observed relationship between corruption
and confidence in public institutions.
A second set of papers improves on these by relying on household-level variation
in survey responses to questions about corruption and confidence to estimate the
correlation between the two. These include Rose, Mishler, and Haerpfer (1998), Mishler
and Rose (2001), Catterberg and Moreno (2005), and Chang and Chu (2006), who all
document a negative partial correlation between perceptions of corruption and confidence
in public institutions in small and regionally-focused samples of countries. These papers
however do not recognize or attempt to address the identification problem to which we
have referred in the introduction: it is unclear from the partial correlations documented
by these authors whether respondents perceptions of corruption drive their confidence in
public institutions, or the converse. Also in this category is a somewhat different, but
related paper by Hellman and Kaufmann (2004), who investigate how an alternative
measure of corruption perceptions influences firms confidence in, and use of, public
institutions. They use data from the World Banks Business Environment and Enterprise
Performance Survey of 6500 firms in transition economies in 2002 to construct a measure
of perceived ,,crony bias as the difference between firms perceptions of their own
influence and the influence of other firms they view as having strong political
connections. They show that firms who perceive a great deal of crony bias in
policymaking have less confidence in the judiciary, are less likely to use courts, are more
likely to pay bribes, and are more likely to cheat on their taxes.
Four more recent papers improve on the ones discussed so far by relying on
respondent-level data on personal experiences with corruption (and not simply
5
perceptions of corruption) to study the effects on confidence in public institutions.
Seligson (2002) uses survey data for four Latin American countries to test the effects of
corruption experiences on perceptions of the legitimacy of the political system at the
individual level. He finds that exposure to corruption erodes belief in the political system
and reduces interpersonal trust. Bratton (2007) uses survey data from 18 African
countries to document that perceptions of corruption are negatively correlated with
respondents satisfaction with public services, but somewhat surprisingly, personal
experience with bribery is positively associated with user satisfaction. However, these
two papers also do not recognize or try to address the identification problem that we have
highlighted in the introduction.3
Finally, Cho and Kirwin (2007) and Lavallée, Razafindrakoto and Roubaud
(2008) use a set of African countries covered by the Afrobarometer survey to investigate
directly the links between confidence in public institutions and both corruption
perceptions and corruption experiences questions. Unlike the rest of the literature
surveyed so far, these papers are the only ones to acknowledge the potential for reverse
causality. Cho and Kirwin (2007) in particular explicitly stress the possibility of vicious
cycles: corruption undermines confidence in public institutions, and this in turn increases
the acceptability of offering bribes to obtain public services, increasing the prevalence of
corruption. Both papers propose using instrumental variables drawn from the same
survey in order to address this identification problem. However, as we explain in more
detail below in our discussion of identification, this strategy depends on the validity of in
our view highly implausible exclusion restrictions that the authors make no effort to
justify.
In summary, the existing literature on the effect of corruption on confidence in
public institutions has been based on small samples of countries, and has for the most part
failed to recognize or address the difficulty of isolating the direction of causation between
corruption and confidence. In the remainder of this paper we show how we can use the
3
The identification problem is compounded by the fact that, despite having record-level data for many
countries, Bratton (2007) does not appear to include country fixed effects in his specifications. This opens
the possibility that unobserved country-level effects are confounding the relationship between corruption
and satisfaction with public services that he studies.
6
very large sample size and the richness of the GWP core questionnaire to make progress
on these issues.
3. Corruption and Confidence in Institutions in the Gallup World Poll
The Gallup World Poll (GWP) has been fielded annually or biennially since 2006
in over 150 countries representing 95% of the worlds adult population, and asks
questions on a wide range of topics. This makes it the largest (in terms of country
coverage) annual multi-country household survey in the world. The surveys are based on
a standard methodology and considerable effort goes into ensuring comparability across
countries. The surveys are designed to be nationally representative of people who are 15
years old or older and great efforts are made to interview households in rural areas, as
well as politically unstable and insecure areas. The surveys are in-depth face-to-face
interviews in all countries except the most developed countries such as Western Europe
or Australia where, for reasons of cost, a shorter version of the survey is fielded by
phone.
The majority of the core questions on the Gallup World Poll are not political in
nature but concern individuals well-being, asking about their everyday lives, level of
happiness, life-satisfaction, expectations about their future, daily experiences of stress,
etc.4 This tends to build a higher level of trust between the interviewer and respondent
than a more technical-sounding government-use questionnaire. Together with an explicit
statement by the enumerator regarding the confidentiality of responses, this likely helps
to improve respondent candor on some of the more sensitive questions in the survey.
We use data from the 2008 and early 2009 waves of the GWP. As our key
measure of corruption we use the following specific question about the respondents
personal experience with corruption: "Sometimes people have to give a bribe or present
in order to solve their problems. In the last 12 months, were you, personally, faced with
this kind of situation, or not (regardless of whether you gave a bribe/present)?" This
4
In this context, we note that a number of recent scholarly papers have used the GWP data for empirical
research. Examples include Deaton (2008, 2009), Helliwell (2008), Ng et al. (2008), Stevenson and
Wolfers (2008), Deaton et al. (2009), Gandelman and Hernández-Murillo (2009), Helliwell et al. (2009),
Krueger and Malecková (2009), and Pelham et al. (2009). The majority of these focus on GWP questions
related to subjective assessments of personal well-being.
7
question, which we will refer to this as the "corruption experiences" question was a new
addition to the core GWP questionnaire in the 2008 wave of surveys. However, for
reasons of timing and questionnaire space, it was asked in only 115 of the 124 countries
covered in our sample of the GWP in 2008 and early 2009. This question was asked in
most high-income OECD, Latin American, Asian and African countries, but coverage of
Eastern Europe is scarcer. Nevertheless the breadth of GWP data still allows us to study
the effects of corruption experiences in a much larger sample of countries and
respondents than any previous work.
The GWP also asks a more generic question about the corruption perceptions of
respondents that we will use alongside the experience question in this paper: "Is
corruption widespread throughout the government in this country, or not?" We refer to
this as the "corruption perceptions" question. It was asked in 112 of the 124 countries in
our sample. However, as the countries in which the corruption experience and perception
questions were fielded do not match perfectly, the sample in which both questions were
asked comprises 103 countries.5
There are substantial conceptual and practical differences between the corruption
experiences and corruption perceptions question. The former asks about a respondents
personal experiences with corruption, while the latter solicits the respondents views
about the prevalence of corruption, regardless of whether the respondent has witnessed or
experienced any corrupt acts himself. We note first that one would naturally expect to
see differences between the responses to the two questions. The corruption experiences
question is potentially a good gauge of "petty" or administrative corruption that
individuals might be likely to experience in their everyday lives: a policeman asking for
a bribe to avoid a ticket, or a bureaucrat soliciting an irregular payment for a permit. On
the other hand, the corruption perceptions question can potentially capture the prevalence
of broader forms of corruption, particularly at higher levels of government. The
downside of course of this latter question is that it does not draw on the respondents
personal experience, but rather is informed by the respondents exposure to second-hand
information about corrupt activities. Finally, as we argue in more detail in Section 4, a
5
See Table 8 for a complete list of countries used in this study.
8
crucial advantage of the corruption experiences question is that it is less likely to suffer
from reverse causality, in the sense that individuals confidence in institutions affects
their corruption experiences. This is important for our interpretation of the empirical
results that follow.
Figures 1 and 2 illustrate the country-level variation in these two measures of
corruption from the GWP. Figure 1 plots country average corruption perceptions versus
corruption experiences. All countries in the sample fall above the 45-degree line,
indicating that average corruption perceptions are higher than average corruption
experiences in every country. In some cases this gap is extreme. Countries such as Japan
or Italy have low rates of personal experience with corruption, but nevertheless strong
perceptions of widespread corruption in government. This suggests low rates of petty or
administrative corruption but a greater incidence of high-level or political corruption. In
Figure 2 we plot the two corruption questions from the GWP against a broad perceptions-
based measure of corruption, the Worldwide Governance Indicators ,,Control of
Corruption variable (Kaufmann et al., 2008). Both corruption questions display a fairly
strong negative correlation with the Control of Corruption measure. However, this
correlation is far from perfect, in part due to the fact that the Control of Corruption
measure aggregates information from a large number of different data sources.
Our main objective in this paper is to document the links between corruption and
confidence in public institutions. We measure the latter using another question in the
GWP, which asks respondents about their confidence in a variety of institutions at the
national level. Specifically, the GWP asks "Do you have confidence in each of the
following?: (a) the military, (b) judicial system and courts, (c) national government, (d)
health care or medical systems, (e) financial institutions or banks, (f) religious
organizations, (g) quality and integrity of the media, and (h) honesty of elections. In our
core specifications we sum together the responses to (a), (b), (c) and (h) to obtain an
index of confidence in public institutions that ranges from 0 (respondents who report no
confidence in any of the four institutions) to 4 (respondents who report confidence in all
four institutions).
9
Figure 3 documents how this measure of confidence in institutions from the GWP
compares with the most closely related variables on confidence in institutions taken from
the World Values Survey.6 While the two measures are highly correlated in the common
sample of countries for which both measures are available (a correlation of 0.81), it is
worth noting the significantly smaller country coverage of the WVS. The circles in the
graph represent countries that are present in our sample of the GWP but not in the most
recent wave of the WVS. Using the GWP index therefore significantly increases the
available sample to study effects of corruption on confidence in institutions.
Finally, Figure 4 documents the relationship between the corruption questions and
the confidence in institutions index at the country level. The top panel plots corruption
perceptions against confidence in institutions and the bottom panel plots corruption
experiences against confidence. Both graphs display a negative relationship between
corruption and confidence although this is much more pronounced for corruption
perceptions. Here, all countries with very low average corruption perceptions score high
on confidence in institutions. Scandinavian countries are the ones with the lowest
perceived corruption and the highest confidence in institutions. Turning to corruption
experiences, we see that in general countries with a higher share of people that have
experienced corruption report lower confidence in institutions. However, there are a
number of countries that have low levels of experienced corruption but still report low
confidence. In this group we find particularly Latin American and Caribbean countries
such as Panama, Argentina, Peru, and Trinidad and Tobago.
6
The WVS asks about respondents confidence in a variety of institutions. We aimed to match this
confidence index as closely as possible to our GWP index and therefore aggregated the answers to the
following four questions into an index ranging from 0 to 4: "I am going to name a number of organizations.
For each one, could you tell me how much confidence you have in them [...]: a) the armed forces, b) the
courts, c) the government (in your nations capital), d) parliament."
10
3. Main Results: Respondent-Level Evidence on Corruption and Confidence in
Institutions
While the cross-country relationship between corruption and confidence in
institutions described above is suggestive of a link between the two, it is also not fully
convincing. A major concern here is that there may be many country-specific factors
driving both variables. For example, some countries may simply have dysfunctional
states. On the one hand this will lead to high levels of corruption, and on the other hand
public institutions naturally do not inspire confidence in such an environment. Any
correlation between our two variables would simply reflect the omitted variable of
government quality that is driving both corruption and confidence in public institutions.
To address this first concern, we primarily focus on the respondent-level variation
within countries to study the relationship between corruption and confidence in
institutions. Doing so allows us to control for any unobserved or observed country-level
characteristics that might be driving the cross-country correlation. Table 1 documents the
distinction between the within- and between-country results. Columns 1 and 3 show
coefficients of cross-country regressions of confidence in institutions on the two
corruption measures, aggregating both variables to the country level. In contrast columns
2 and 4 report on the corresponding regressions including country fixed effects.7 In all
cases we find a negative correlation between corruption and confidence in institutions
that is highly statistically significant. In the cross-country variation, the estimated
coefficients imply that a one-standard-deviation increase (across countries) in either of
the two corruption measures reduces confidence in institutions by between 0.3 and 0.4
points on a 0-4 scale.8 Within countries, the relationship between corruption and
confidence is also very strong. Here a one standard deviation increases of either
corruption variable within a country leads to a reduction of confidence in institutions of
7
It would technically be more appropriate to estimate an ordered probit model because of the discrete and
ordered nature of our dependent variable. Doing this does not change the sign or level of significance of the
coefficients. However, because of the difficulties involved with interpreting ordered probit coefficients as
marginal effects, we chose to present linear regression results throughout the paper.
8
Note that the cross-country standard deviations of corruption experiences and perceptions are 0.083 and
0.184, respectively.
11
between 0.1 and 0.3 points.9 Anticipating our later discussion of endogeneity problems,
we note that the estimated effect of the corruption perceptions question is nearly three
times as large as the effect of the corruption experiences question. This is consistent with
our view that the former is much more likely to be endogenous to respondents
confidence in public institutions, and that the latter much more plausibly identifies a
causal effect running from corruption to confidence. While the estimated coefficients are
statistically significant and quantitatively large, we note that the explanatory power of
corruption for the confidence question is limited. In particular, in the fixed-effects
regressions, the bulk of the R-squared is due to the country dummies. In contrast, the
within R-squared net of the country fixed effects is 0.01 for the corruption experiences
question, and 0.06 for the corruption perceptions question.
Although within-country regressions in Table 1 control for country-level omitted
variables, a possible objection is that there may also be a variety of individual-specific
characteristics that influence respondents' confidence in institutions and the likelihood
that they view corruption as prevalent, or that they report having been solicited for a
bribe. For example, richer, older, and more educated people might have more
interactions with the state and so be more likely to find themselves exposed to corruption,
and might also be more likely to have a cynical world view that precludes expressing
confidence in public institutions.
To control for this we introduce a set of core control variables that we have found
to be correlated with the corruption questions, and that also tend to be significant
predictors of confidence in institutions. These include respondent age, gender, marital
status, education, and income. We also introduce as basic control variables whether the
household in which the respondent lives has access to the internet and a television.
Access to such media may have ambiguous effects on individuals opinions about and
experiences with corruption and institutions. On the one hand, officials might have a
harder time extracting bribes from more informed citizens that have had the chance to
obtain information about laws and regulations concerning their dealings with
government. On the other hand, coverage of corruption cases in the media might
9
Within-country standard deviation of corruption experiences is 0.360 and of corruption perceptions 0.368.
12
influence corruption perceptions of individuals and may therefore have a direct effect on
the answers to the perceptions question used in the GWP.
Table 2 presents the results. We note first that missing data presents a problem
when introducing our set of core control variables. In particular, data availability for
education and income is an issue and decreases our sample to about 57,000 individuals in
94 countries. To aid in comparison with the previous results, we first repeat the results
with no controls from Table 1 in the smaller sample for which the control variables are
available, and then report results with controls. Reducing the size of the sample in this
way makes little difference for the effect of corruption on confidence in public
institutions: the results without control variables in columns (1) and (3) of Table 2 are
essentially identical to those in columns (2) and (4) of Table 1. Second, we note that
while the additional control variables featured in Table 2 do show some correlation with
both the corruption and confidence variables, we find that the estimated coefficients on
the corruption variables change very little, declining just slightly in absolute value.
Finally, we note that the control variables all enter with expected signs and are generally
significant. Older individuals seem to have a lower degree of confidence in institutions
although this relationship is not linear. Also, married respondents express higher
confidence than single ones. Higher income and education as well as access to internet
and TV appear to reduce confidence although these latter effects are not statistically
significant in all cases.
While the results in Table 2 are suggestive of a strong relationship between
confidence in institutions on the one hand, and corruption perceptions and experiences on
the other, one might nevertheless reasonably worry that this correlation is driven by other
unobserved respondent-specific characteristics. A leading possibility is that, conditional
on the basic control variables described above, some individuals may simply have a
negative outlook or worldview which makes them more likely to think that corruption is
widespread, and at the same time drives their lack of confidence in public institutions.
Kaufmann and Wei (2000) coin this as a "kvetch" effect, after the Yiddish word for
habitual complaining. To the extent that this drives the observed correlation between
13
corruption and confidence in public institutions, we cannot interpret it as a causal link
from the former to the latter.10
At first glance, one might think that this potential problem of "kvetch" is less
severe for the corruption experiences question than for the corruption perceptions
question. While ostensibly an objective question about the respondent's experience, there
are nevertheless ways in which kvetch might creep into responses to this question as well.
First, respondents prone to kvetch might simply falsely claim that they had been solicited
for a bribe. They might also be more likely to interpret the fine line between tips and
bribes in the direction of the latter. Therefore, respondents who in general tend to
complain a lot might also be more likely to report interactions with public officials as
involving a request for a bribe. Second, the question about experiences with bribery
follows a battery of other questions about corruption, one of which is the corruption
perceptions questions described above. It is possible that respondents prone to kvetch
want to enforce their point of stating that government corruption is a problem by
answering that they personally have found themselves in a bribe situation.
Our strategy for dealing with this problem is to introduce control variables that we
think may be good proxies for the propensity to kvetch. We consider three sets of such
proxies.11 The first set relies on questions in the survey that focus on individuals' self-
reported well-being. For example, the GWP asks respondents whether they are satisfied
with their living standards, and which rung on the ladder of life that they find themselves.
The GWP also asks respondents whether they have felt a variety of emotions such as
worry, stress, or happiness in the previous day. These variables are plausibly correlated
with individual respondents' predisposition to complain. Second, the GWP asks
respondents their opinions about a number of country-level variables including whether
the economy is doing well or poorly, whether the economic outlook is favorable, and
whether corruption is getting better or worse. Since our regressions include country fixed
effects that soak up all national-level variation, variation in individuals' responses to these
10
Newton and Norris (2000) examined the question if trust and confidence is a feature of basic personality
types but found little evidence to support this thesis.
11
See table 7 for a detailed description of the kvetch proxies and the specific GWP questions used in their
construction.
14
questions can be interpreted as capturing their idiosyncratic perceptions of the same
national-level reality, and as such will also plausibly be correlated with kvetch.
As a final control for kvetch, we note that the battery of questions from which our
"confidence in institutions" variables are drawn includes a further question about
confidence in religious organizations. It seems plausible to us that corruption perceptions
or experiences are likely to have little direct impact on confidence in religious
organizations. However there might be an indirect effect through kvetch: individuals
more likely to complain in general might also report less confidence in religious
organizations purely because of their propensity to kvetch. This suggests using a kind of
differencing strategy to control for kvetch. In particular, one might ask whether
corruption reduces the difference in confidence in public institutions and confidence in
religious organizations. Alternatively and more flexibly, we can simply introduce
confidence in religious organizations directly into our main specification as a control for
kvetch.
Table 3 documents the results controlling for these proxies for kvetch. Since not
all of the kvetch variables are available for all observations, our sample shrinks further to
49,019 respondents in 90 countries. As in Table 2, we first document that our main
results with basic respondent-level controls do not change as we move to this smaller
sample (compare columns (1) and (3) in Tables 2 and 3). More interesting is how our
results on the effects of corruption perceptions and experiences on confidence in
institutions change when we control for ketch. We find that the estimated impact of
corruption on confidence falls by about 34 percent (for the corruption experiences
question) and by 40 percent (for the corruption perceptions question). This is a good
indication that kvetch effects are present in the data and are at least partially addressed by
the controls that we introduce. Interestingly, while both the corruption perceptions and
corruption experiences questions might be subject to kvetch, we think it is plausible that
kvetch effects are stronger for the former. The results in Table 3 are consistent with this:
the coefficient on the corruption perceptions falls relatively more after the introduction of
the kvetch controls. However, even after introducing these very rigorous controls for
15
kvetch, the negative relationship between corruption and confidence remains highly
significant and the magnitude of both corruption coefficients stays impressive.
4. Robustness of the Main Results
Thus far we have seen that there is a large and statistically significant partial
correlation between measures of corruption and confidence in public institutions, and that
this result is robust to the addition of (a) country fixed effects, (b) a set of respondent-
level controls, and (c) a set of proxies for ,,kvetch. In this section we subject these main
results to a variety of further robustness checks. We first disaggregate the confidence in
institutions measure into its four components and investigate how the effects of
corruption vary across these components. We then also estimate our main specification
country-by-country, and document how the estimated coefficients on the corruption
questions vary by country, by level of corruption, and by level of development. Finally,
we discuss the extent to which this robust partial correlation between corruption and
confidence that we have documented can be interpreted as a causal effect from the former
to the latter.
In Table 4 we disaggregate the confidence in institutions measure into its four
components: confidence in the military, judiciary, national government, and in the
honesty of elections. In the first four columns we report results for our core specification,
using each of these components of the overall confidence measure separately as the
dependent variable.12 We do this for both the corruption experiences (top panel) and
corruption perceptions measure (bottom panel). In all cases, we include, but do not
report estimated coefficients for, the full set of control variables used in Table 3. For the
corruption experiences question, we find only modest differences across components in
terms of the magnitude of the estimated partial correlation between corruption and
confidence. This effect is largest for confidence in the judiciary at 0.06, and smallest for
confidence in the honesty of elections, at 0.04. There is somewhat more variation across
12
Since the dependent variable for the individual confidence in institutions regressions is a binary variable,
a probit specification would be more appropriate than the linear probability model that we report (for
consistency with previous results). We have also estimated the specifications in Table 4 using a probit
model and find a similar pattern of relative magnitudes of the effect of corruption on the different
confidence in institutions variables.
16
the various confidence measures for the corruption perceptions question. The estimated
effect of corruption is much lower for confidence in the military, at 0.06, than it is for the
other three measures, which range from 0.13 to 0.17.
Thus far we have assumed that the slope of the relationship between corruption
and confidence in public institutions is the same in all countries, at all income levels, and
at all levels of corruption. We now relax this assumption and re-estimate our main
specification from Table 3, country-by-country, so that we can investigate how this slope
varies across countries. We note first that the means of the country-by-country estimates
in Table 5 are slightly smaller than the pooled estimates in Table 3 (at -0.13 and -0.47 for
the corruption experiences and perceptions questions, respectively). The sign of the
estimated coefficient is also fairly consistently negative across countries, with 67 percent
(91 percent) of country estimates being negative for the corruption experiences
(perceptions) question. However, and not surprisingly, in many countries the estimated
effects are not statistically significant, given the much smaller sample of observations on
which to base inference in each country. In fact, the mean number of observations per
country for the regressions in Table 5 is just 594, as opposed to 49,019 in the pooled
regressions of Table 3.
We next examine how these estimated coefficients vary across regions (using the
standard World Bank regional classification). While it is evident that corruption
experiences as well as perceptions affect confidence negatively in all regions on average,
the magnitude and strength of the relationship varies widely across regions, from -0.06 to
-0.32 in the case of corruption experiences, and from -0.10 to -1.00 in the case of
corruption perceptions. In the case of corruption experiences, the largest mean estimated
effect is for the South Asia region. The relationship between corruption and confidence
in institutions is also the strongest in this region with 60 percent of countries reporting a
statistically significant negative relationship. At the same time however, while South
Asia showed the largest coefficient of corruption experiences, its perceptions coefficient
is the smallest among the regions in our sample.
In the remaining panels of Table 5 we document how the estimated correlation
between corruption and confidence varies with the average level of corruption, and the
17
level of development, of the country. To do this, we divide countries into three equal
groups according to their country-level average score on the corruption question, and also
their level of GDP per capita. We then report the mean (across countries) of the
estimated slope coefficient on corruption from the country-by-country regressions, for
each group. In the case of corruption experiences, there is a pronounced non-linear
relationship in countries overall level of corruption. In countries where reported
corruption experiences are on average either very low or very high, the estimated effect
of corruption experiences on confidence in institutions is small (at 0.07 and 0.09
respectively). In contrast, for intermediate-corruption countries, the adverse effect of
corruption on confidence is much larger.
This suggests that in countries where corruption is rare, a respondent's isolated
experience with having been solicited for a bribe will not be enough to substantially
undermine his or her faith in overall public institutions. And similarly, in countries where
corruption is widespread, personal experiences with or perceptions of corruption might
also not change confidence in public institutions because this confidence is very low to
begin with. In contrast, for countries with a moderate prevalence of corruption, personal
experiences with corruption have a stronger adverse impact on confidence in public
institutions. Interestingly, however, this pattern is not present in the corruption
perceptions question, nor is it present when countries are divided into groups according to
income levels.
5. Concerns About Endogeneity
We now discuss the extent to which the partial correlation between corruption and
confidence in public institutions can be interpreted as a causal effect from the former to
the latter. As noted in the introduction, there is an important identification problem:
corruption might lead to a loss of confidence in public institutions as we emphasize here,
but at the same time, respondents who report low confidence in public institutions might
as a result hold the belief that corruption is widespread as well. This point is also noticed
by Cho and Kirwin (2007) who argue that individuals who do not trust public institutions
might be more likely to resort to bribery to advance their interests, or to believe that
corruption is widespread. This can lead to vicious circles where corruption and a lack of
18
confidence in public institutions feed off each other. This potential for bi-directional
causation complicates the interpretation of the partial correlation between corruption and
confidence in institutions that we have documented. This is the classic identification
problem: the observed correlation between corruption and confidence might reflect
causal effects from corruption to confidence that we emphasize. But it could also reflect
causation in the opposite direction.
We note first that a unique strength of the corruption experiences question is that
it is much less likely to be prone to reverse causation than the corruption perceptions
question. To see why, recall that the experience question asks respondents whether they
have been solicited for a bribe during the past 12 months. To the extent that the decision
to solicit a bribe originates with the public official with whom the respondent is
interacting, there should be no problems of reverse causation. It seems unlikely that a
public official would even know the respondents confidence in public institutions, let
alone base his decision to solicit a bribe on it. This stands in contrast with the corruption
perceptions question, where there is a more plausible channel of causation in the opposite
direction: individuals who have low confidence in public institutions may precisely for
this reason also believe that corruption is widespread in government. This potential
endogeneity bias may in part account for the fact that in most of our specifications thus
far, the estimated slope of the relationship between corruption perceptions and confidence
is larger in absolute value, and typically is also much more significant, than in the
regressions using the corruption experiences question. Thus we argue that our results
using the corruption experiences question provide a fairly plausible estimate of the causal
effect of corruption on confidence in public institutions.
At the same time, we acknowledge that there may still be such endogeneity bias,
although to a lesser extent, even in the corruption experiences question. This would
occur if respondents expressing a low confidence in public institutions are more likely to
interpret an ambiguous interaction with a public official as a request for a bribe than other
respondents with higher confidence in public institutions. Such potential endogeneity
bias is extremely difficult to correct using purely cross-sectional observational data such
as what we have in the GWP. The usual strategy with observational data of identifying
19
instruments (variables that plausibly affect only corruption, but not confidence in
institutions, and vice versa) is very difficult to implement since it is hard to make a
compelling case for the requisite exclusion restrictions.
In particular, we find it hard to make a convincing case that there are variables in
the GWP that predict corruption at the individual level but do not have predictive power
for confidence in institutions that we could then use as instruments for corruption. To
illustrate why we think this approach is not promising, consider the identifying
assumptions implicit in the few papers that have considered this reverse causation
problem. Cho and Kirwin (2007) make the identifying assumption that variables such as
respondents overall trust in others, and their perceptions of the political influence of
ethnic groups, matter only for corruption and has no direct effect on confidence in
institutions (see their Table 1). Lavallee, Razafindrakoto, and Robaud (2008) claim with
little justification that a dummy variable indicating that the respondent is head of the
household, and a variable capturing the respondents views on the acceptability of paying
a bribe, matter only for corruption and have no direct effect on confidence. We do not
find such exclusion restrictions to be convincing. One might easily imagine that any of
these variables are directly correlated with confidence in public institutions: for example
respondents might believe that paying a bribe is acceptable precisely because they have
no confidence in public institutions. It is also striking that in both papers, the
instrumented estimates of the effects of corruption on confidence are vastly larger in
absolute value than the uninstrumented estimates, while the feedback problem these
authors seek to correct would suggest that the true effects of corruption on confidence
should be much smaller in absolute value than the corresponding OLS estimates (see
columns (1) and (2) of Table 1 in Cho and Kirwin (2007) and Table 4 in Lavalee,
Razafindrakoto and Robaud (2008)). These counterintuitive results likely signal nothing
more than a failure of the exclusion restrictions required to justify the instrumental
variables estimator.13 14 In contrast, we have consistently found that the magnitude of the
13
Lavalee, Razafindrakoto and Robaud (2008) claim support for their identification strategy in the fact that
tests of overidentifying restrictions fail to reject the null of instrument validity. Here they fall into the
(unfortunately common) pitfall of failing to realize that such tests are valid only if at least one instrument is
indeed valid. We think it is very difficult to make such a case in this context.
20
effect of the more exogenous corruption experiences question on confidence is always
substantially smaller than the effect of the corruption perceptions question, consistent
with the view that the former is less tainted by reverse causation.
Absent compelling instruments, we use an argument based on Leamer (1981) to
provide a rough bound on the extent to which our estimates might reflect reverse
causation. To make this concrete let y denote the portion of confidence that is orthogonal
to all of the control variables, including the country fixed effects, in columns 2 and 4 of
Table 3, and let x denote the same orthogonal component of corruption. The possibility
of causal effects in both directions between corruption and confidence can be captured by
the assumption that y and x are generated by the following system of two equations:
(1)
We are primarily interested in the slope coefficient which captures the effect of
corruption on confidence. However, we cannot identify this effect absent some
instrument that shifts corruption without at the same time affecting confidence, i.e. we
need to find a variable that is included in the second equation but excluded from the first.
Absent such an instrument, the problem is simply that there are four unknown
parameters in this system ( , , and the two variances of the error terms), while there are
just three moments in the data (V(x), V(y), and COV(x,y)).15 However, we can still make
progress by exploring how our estimate of would change given differing assumptions
on the strength of the reverse causation captured by . To do this, express the three
observable data moments in terms of the four unknown parameters, and then solve for
conditional on a value of . Then by varying we can explore the robustness of our
14
An alternative approach sometimes used with survey data is to use the average of the corruption question
across all observations within a pre-specified group, for example all respondents in the same city, as an
instrument for corruption. This is plausible as an identification strategy only to the extent that we think that
the unexplained portion of confidence is uncorrelated across respondents within a group. This assumption
is difficult to justify in practice.
15
In fact things might be even more complicated, as we have assumed for simplicity that the covariance
between the two structural errors is zero as well. We justify this simplifying assumption by observing that
in Table 3 we have already controlled for a large set of variables that might simultaneously be driving
corruption and confidence. Thus it is more plausible that the errors in the orthogonalized system here are
independent.
21
conclusions about to alternative assumptions regarding the strength of the reverse
causation. Some simple algebra delivers this very natural estimator for as a function of
:
(2)
Note that when we retrieve the OLS estimator, i.e. , since in
this case there is no feedback from confidence to corruption, and so OLS is valid. On the
other hand, note that when which is simply the OLS
estimate of the feedback effect in the second equation. This is because if there is in fact
no causal effect running from corruption to confidence, then the second equation can be
estimated by OLS.16 Moreover, the range from =0 to seems to us
to be a reasonable prior bound for the magnitude of reverse causation. It seems
reasonable to assume that <0, i.e. less confidence implies more corruption. However,
the magnitude of this effect is likely to be less (in absolute value) than
. If it were not, then the data would imply that >0, i.e. that
corruption raises confidence in public institutions, which seems implausible.
We plot this estimate of (on the vertical axis) as a function of (on the
horizontal axis) in Figure 5, using this prior plausible range of values for the magnitude
of reverse causation. The top panel refers to the corruption experiences question, and the
bottom to the corruption perceptions question. In both panels, when =0 we retrieve the
OLS estimates of on the horizontal axis corresponding to those in Columns (2) and (4)
of Table 3. As we allow for the possibility of more and more reverse causation, i.e. as
becomes more and more negative capturing a stronger effect of confidence on corruption,
our estimate of the main effect of interest, , becomes closer and closer to zero. We also
report 95 percent confidence intervals for , and these suggest that our estimate of
would be insignificantly different from zero only if were very large (in absolute value).
In particular, we note that the 95 percent confidence interval for includes zero only
16
While rarely used, it is interesting to note that the basic argument here is nearly 80 years old! Leamer
(1981) credits Leontief (1929) with first performing this basic calculation.
22
when opportunity, would you like to move permanently to another country, or would you prefer
to continue living in this country?"
In Table 6 we document the relationship between corruption, confidence, and
these three outcomes. In the first column, we report the simple bivariate relationship
between the confidence variable and the three outcome variables of interest, and in the
second column we introduce the full set of control variables from Table 3. We find
strong evidence that a lack of confidence in public institutions raises sympathy for violent
protest, raises the desire to migrate, and reduces political participation. We next
investigate the extent to which this reflects the effect of corruption perceptions and
corruption experiences. In columns three and four we estimate regressions of the three
variables on the two corruption variables alone (but still controlling for the full set of
control variables from Table 3). Here we find evidence those individuals who have
experienced corruption or who perceive corruption to be high in their country show
support for violent protest and express increased desire to permanently leave their
country. In addition, we find that having had a corruption experience lowers the
likelihood of individuals voicing their opinion to public officials.
Finally, we introduce both corruption measures together with confidence in
institutions as explanatory variables. Doing so sheds light on whether the effects of
corruption on these outcomes operate only through confidence in institutions (in which
case the corruption variables would not enter significantly), or whether there are direct
effects of corruption (in which case they would enter significantly even after controlling
for confidence in public institutions). In the case of corruption experiences, there seems
to be fairly clear evidence of both direct and indirect effects, as both the corruption and
confidence variables enter significantly. In the case of corruption experiences however
the effects seem to run more through confidence in institutions. These findings provide
some support to the findings of Putnam (2000) and Uslaner (2002) that institutional trust
contributes to citizens involvement in the political process.
7. Conclusions
In this paper we have used data from the Gallup World Poll, a unique and very
large global household survey, to document a quantitatively large and statistically
24
significant negative effect of corruption on confidence in public institutions. This
highlights an important, but relatively under-examined, channel through which corruption
can inhibit development. Our findings are robust to the inclusion of a large set of
controls for country and respondent-level characteristics. In addition to considering a
much larger sample of countries and a more thorough set of control variables, our main
contribution relative to the existing literature is our treatment of potential endogeneity
biases. We have argued that a key advantage of specific experiential questions about
corruption is that they are much more plausibly exogenous to respondents reported
confidence in public institutions. As a result, the partial correlation between such
questions and confidence can much more plausibly be interpreted as a causal effect from
the former to the latter.
25
References
Acemoglu, Daron, Simon Johnson, and James A. Robinson (2001): The Colonial Origins
of Comparative Development: An Empirical Investigation, American Economic Review,
91(5), 1369-1401.
Anderson, Christopher J. and Yuliya V. Tverdova (2003): Corruption, Political
Allegiances, and Attitudes toward Government in Contemporary Democracies, American
Journal of Political Science, 47(1), 91-109.
Bratton, Michael (2007): Are You Being Served? Popular Satisfaction with Health and
Education Services in Africa, Afrobarometer, Working Paper No. 65.
Catterberg, Gabriela and Alejandro Moreno (2005): The Individual Bases of Political
Trust: Trends in New and Established Democracies, International Journal of Public
Opinion Research, 18(1), 408-443.
Chang, Eric C.C. and Yun-han Chu (2006): Corruption and Trust: Exceptionalism in
Asian Democracies?, The Journal of Politics, 68(2), 259-271.
Cho, Wonbin and Matthew F. Kirwin (2007): A Vicious Cycle of Corruption and
Mistrust in Institutions in Sub-Saharan Africa: A Micro-Level Analysis, Afrobarometer
Working Paper, No. 71, Michigan State University.
Deaton, Angus (2008): Income, Health, and Wellbeing Around the World: Evidence from
the Gallup World Poll, Journal of Economic Perspectives, 22(2), 53-72.
Deaton, Angus (2009): Aging, Religion, and Health, National Bureau of Economic
Research Working Paper No. 15271.
Deaton, Angus, Jane Fortson, and Robert Tortora (2009): Life (Evaluation), HIV/AIDS,
and Death in Africa, National Bureau of Economic Research Working Paper No. 14637.
Della Porta, Donatella (2000): Social Capital, Beliefs in Government, and Political
Corruption, in: Pharr, Susan J. and Robert D. Putnam eds.: Disaffected Democracies:
What's Troubling the Trilateral Countries?, Princeton, NJ: Princeton University Press.
Easton, David (1965): A Systems Analysis of Political Life, New York: John Wiley.
Easton, David (1975): A Re-Assessment of the Concept of Political Support, British
Journal of Political Science, 5(4), 435-457.
Gandelman, Néstor and Rubén Hernández-Murillo (2009): The Impact of Inflation and
Unemployment on Subjective Personal and Country Evaluations, Federal Reserve Bank
of St. Louis Review, 91(3), 107-126.
Gibson, James L. and Gregory A. Caldeira (1995): The Legitimacy of Transnational
Legal Institutions: Compliance, Support, and the European Court of Justice, American
Journal of Political Science, 39(2), 459-89.
Gibson, James L., Gregory A. Caldeira, and Lester K. Spence (2003): Measuring
Attitudes toward the United States Supreme Court, American Journal of Political
Science, 39(2), 354-367.
26
Helliwell, John F. (2008): Life Satisfaction and Quality of Development, National Bureau
of Economic Research Working Paper No. 14507.
Helliwell, John F., Christopher P. Barrington-Leigh, Anthony Harris, and Haifang Huang
(2009): International Evidence on the Social Context of Well-Being, National Bureau of
Economic Research Working Paper No. 14720.
Hellman, Joel and Daniel Kaufmann (2004). "The Inequality of Influence". Available at
SSRN: http://ssrn.com/abstract=386901 or doi:10.2139/ssrn.386901.
Kaufmann, Daniel and Shang-Jin Wei (2000): Does "Grease Money" Speed Up the
Wheels of Commerce?, IMF Working Papers, 00/64, Washington DC: International
Monetary Fund.
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi (2008): Governance Matters VII:
Aggregate and Individual Governance Indicators 1996-2007, Policy Research Working
Paper, 4654, Washington DC: The World Bank.
Kaufmann, Daniel and Joel Hellman (2004): "Political Inequality and the Subversion of
Institutions in Transition Economies", in Janos Kornai and Susan Rose-Ackerman (eds).
"Building a Trustworthy State in Post-Socialist Transition", Palgrave-MacMillan.
Knack, Stephen and Philip Keefer (1995): Institutions and Economic Performance:
Cross-Country Tests Using Alternative Institutional Measures, Economics and Politics,
7(3), 207-227.
Knack, Stephen and Philip Keefer (1997): Why Dont Poor Countries Catch Up? A
Cross-National Test for an Institutional Explanation, Economic Inquiry, 35(3), 590-602.
Krueger, Alan B. and Jitka Malecková (2009): Attitudes and Action: Public Opinion and
the Occurrence of International Terrorism, Science, 235, 1534-1536.
Lambsdorff, Johann Graf (2007): The Institutional Economics of Corruption and Reform:
Theory, Evidence, and Policy, Cambridge: Cambridge University Press.
Lavallee, Emmanuelle, Mireille Razafindrakoto, and Francois Roubard (2008) :
Corruption and Trust in Political Institutions in Sub-Saharan Africa, Afrobarometer,
Working Paper No. 102.
Leamer, Edward E. (1981): Is it A Supply Curve or Is it a Demand Curve? Partial
Identification Through Inequality Constraints, Review of Economics and Statistics, 63(3),
319-327.
Leontief, Wassily (1929): Ein Versuch Zur Statistischen Analyse von Angebot und
Nachfrage (An Inquiry on the Statistical Analysis of Supply and Demand),
Weltwirtschaftliches Archiv, 30(1), 1-53.
Mauro, Paolo (1995): Corruption and Growth, The Quarterly Journal of Economics,
110(3), 681-712.
Meon, Pierre-Guillaume and Khalid Sekkat (2004): Does the Quality of Institutions Limit
the MENAs Integration in the World Economy, The World Economy, 27(9), 1475-1498.
27
Meon, Pierre-Guillaume and Khalid Sekkat (2005): Does Corruption Grease or Sand the
Wheels of Growth?, Public Choice, 122(1), 69-79.
Mishler, William and Richard Rose (2001): What are the Origins of Political Trust?
Testing Institutional and Cultural Theories in Post-Communist Societies, Comparative
Political Studies, Vol. 34(1), 30-62.
Mishler, William and Richard Rose (2005): What Are the Consequences of Trust? A Test
of Cultural and Institutional Theories in Russia, Comparative Political Studies, 38(9),
1050-1078.
Mo, Pak Hung (2001): Corruption and Growth, Journal of Comparative Economics,
29(1), 66-79.
Newton, Kenneth and Pippa Norris (2000): Confidence in Public Institutions: Faith,
Culture, or Performance?, in: Pharr, Susan J. and Robert D. Putnam: Disaffected
Democracies: What's Troubling the Trilateral Economies, Princeton, NJ: Princeton
University Press.
Ng, Weiting, Ed Diener, Raksha Aurora, and James Harter (2008): Affluence, Feelings of
Stress, and Well-Being, Social Indicators Research, doi: 10.1007/s11205-008-9422-5.
North, Douglass C. (1990): Institutions, Institutional Change, and Economic
Performance, New York: Cambridge University Press.
Pelham, Brett, Steve Crabtree, and Zsolt Nyiri (2009): Technology and Education,
Harvard International Review, Summer, 74-76.
Pellegrini, Lorenzo and Reyer Gerlagh (2004): Corruptions Effects on Growth and its
Transmission Channels, Kyklos, 57(3), 429-456.
Pharr, Susan (2000): Officials Misconduct and Public Distrust: Japan and the Trilateral
Democracies, in: Pharr, Susan J. and Robert D. Putnam eds.: Disaffected Democracies:
What's Troubling the Trilateral Countries?, Princeton, NJ: Princeton University Press.
Putnam, Robert D. (2000): Bowling Alone, New York: Simon & Schuster.
Rodrik, Dani, Arvind Subramanian, and Francesco Trebbi (2004): Institutions Rule: The
Primacy of Institutions over Geography and Integration in Economic Development,
Journal of Economic Growth, 9(2), 131-165.
Rose, Richard, William Mishler, and Christian Haerpfer (1998): Democracy and its
Alternatives: Understanding Postcommunist Societies, Baltimore: The Johns Hopkins
University Press.
Seligson, Mitchell A. (2002): The Impact of Corruption on Regime Legitimacy: A
Comparative Study of Four Latin American Countries, The Journal of Politics, 64(2),
408-433.
Stevenson, Betsey and Justin Wolfers (2008): Economic Growth and Subjective Well-
Being: Reassessing the Easterlin Paradox, Brookings Papers on Economic Activity,
Spring, 1-87.
28
Uslaner, Eric M. (2002): The Moral Foundations of Trust, Cambridge: Cambridge
University Press.
29
Figure 1: GWP Corruption Perceptions and Experiences
Correlation of corruption perceptions and experiences
1
Chad Tanzania
Trinidad & Tobago Cameroon
Kenya
Indonesia Lithuania
Country average corruption perceptions
Israel Uganda
Paraguay Lebanon
Zambia
Peru
Sierra Leone
PanamaSenegal Liberia
Ethiopia Ukraine
Nepal Mongolia Hungary
Burundi
India
Italy Honduras
Thailand
.8
Argentina
MalaysiaIran Moldova
Mexico
JapanCosta Rica Armenia
Bangladesh
Ghana TogoRussia
Cambodia
Philippines
Latvia
Madagascar
Guatemala
Portugal
Sri Taiwan
Nicaragua
LankaHaiti
South Korea
Colombia Faso
Burkina Algeria
Botswana
Poland Mauritania
Pakistan Venezuela
Ecuador
Salvador Republic
El Chile Bolivia
Dominican
Niger
Brazil
Malta
.6
VietnamDjibouti Azerbaijan
Turkey
France
Ireland
United Kingdom
Austria
Germany
Estonia
Spain
Belgium Laos
Uruguay Belarus
Canada
.4
Australia
Netherlands
Norway Zealand
New
Luxembourg
Sweden
.2
Denmark
Finland
0
0 .2 .4 .6 .8 1
Country average corruption experiences
30
Figure 2: Correlation of GWP Corruption Experiences and Perceptions Questions
with Worldwide Governance Indicators (WGI) ,,Control of Corruption Variable
GWP corruption experiences - WGI
Algeria
.4
Hungary
Corruption experiences
Azerbaijan
Cameroon
.3
Liberia
Ukraine Russia
Kenya Tanzania
Moldova Lebanon
Armenia
Lithuania
Latvia
Togo
Haiti Belarus
Mongolia
Bolivia Mauritania
.2
Uganda
Iran
Djibouti
VenezuelaZambia
Ecuador
ChadSenegal IndiaLeone Laos
Mexico
Peru Sierra
Ethiopia
Burkina Faso
Paraguay Philippines Taiwan
Cambodia ThailandTurkey
Burundi Madagascar Finland
Nicaragua
Dominican
Guatemala Republic
Vietnam
IndonesiaGhana Costa Rica Israel Chile
SouthHonduras Sri Lanka
Korea Botswana
.1
Nepal El Salvador
Niger Colombia Estonia
Spain New Zealand
Poland
Bangladesh Brazil Malaysia
Argentina Uruguay Belgium Australia
Canada
Sweden
Portugal
Norway Panama & Tobago France Ireland Kingdom
Malta Austria
NetherlandsTrinidad Italy Germany Denmark
Pakistan
United Luxembourg
Japan
0
-1 0 1 2 3
WGI
Fitted Values
GWP corruption perceptions - WGI
1
ChadCameroon Trinidad & Tobago
Kenya Tanzania
Indonesia Israel
ParaguayLithuania Lebanon
Uganda
Zambia
Peru Sierra Leone
Senegal Panama
Liberia
Ethiopia
Mongolia
Ukraine
Burundi NepalIndia
Honduras
Thailand Italy
Hungary
.8
Argentina
IranMoldova
Corruption perceptions
MexicoMalaysia
Togo Armenia Ghana Taiwan
Bangladesh
Cambodia Philippines Costa Rica Russia
Latvia
Portugal Sri Lanka
Guatemala Madagascar Japan
Nicaragua
HaitiSouth Korea Colombia
Burkina Faso
Algeria
Venezuela
Ecuador Mauritania Botswana
Poland Pakistan
El
Bolivia Salvador
Djibouti
Dominican Republic
Brazil Finland
Malta
.6
Vietnam
Azerbaijan Laos Chile
France Ireland
United Kingdom
Austria
Germany
Estonia Belgium
Spain
Belarus Turkey
Uruguay Canada
.4
Netherlands
Norway AustraliaZealand
New
Luxembourg
Sweden
.2
Denmark
Niger
-1 0 1 2 3
WGI
Fitted Values
31
Figure 3: Comparing Confidence in Institutions: Country Average Values of GWP
and WVS Indices
Confidence in institutions: GWP - WVS
4
Botswana
Fin la nd
3
Denmark
Ban gl ad esh Vietnam
RussiaMada ga scar
Luxembourg Ghana
Armenia India
Norway de n
GWP
Swe Turkey
France Ma laysia
United
Thailand Kingdom
Zambia
Tanzania Spain Indonesia
Germany Iran
2
Senegal
Colombia
Ven ezuela
Belgium
Poland Australia
Burkina Faso
Italy
Mexico
Chile
Ecu ad or South
NetherlandsKorea
Hungary Ethiopia
Taiwan
Arge ntina
1
Chad
Peru Moldova
Haiti Ukraine
Togo
0 1 2 3 4
WVS
32
Figure 4: Confidence in Institutions and Corruption Experiences/Perceptions
Confidence in institutions and corruption perceptions
Botswana
Finland
3
Denmark Laos Vietnam Bangladesh
Netherlands Madagascar Sierra Leone
Luxembourg Canada Ghana
2.5
Australia Ireland
Djibouti
Azerbaijan India
Sweden Norway Turkey SriCambodia
Lanka
France Nepal
Malaysia
Burundi
United Kingdom Zambia
New ZealandSpainGermanyNiger Philippines Indonesia
ThailandTanzania
Uruguay Iran Senegal
2 Austria Colombia
Algeria
Uganda
Belgium Japan
Costa Liberia
Venezuela Rica
Pakistan Republic Kenya
Dominican Armenia Lebanon
Burkina Faso
MaltaPoland Mexico Israel
Brazil Portugal
1.5
Chile Nicaragua Italy
Belarus
Estonia Ecuador Korea
ElMauritania
South
Russia
Salvador
Ethiopia
Hungary Cameroon
Paraguay
Bolivia GuatemalaPanama
Honduras
Taiwan Mongolia
Argentina Chad
1
Latvia Lithuania
Moldova Trinidad & Tobago
Peru
Haiti Ukraine
Togo
.5
.2 .4 .6 .8 1
Country average corruption perceptions
Fitted Values
Confidence in institutions and corruption experiences
Botswana
Country average confidence in institutions
Finland
3
Denmark Laos
Vietnam
Bangladesh
Netherlands MadagascarSierra Leone
Luxembourg
Canada Ghana
2.5
Ireland
Norway
Australia India Djibouti Azerbaijan
Sweden Cambodia
Turkey
Sri
France Lanka
NepalBurundi
Malaysia
United Kingdom
Indonesia Zambia
Thailand Tanzania
New Philippines
SpainZealand
Uruguay Niger Senegal
Germany Iran
2
Colombia
Austria Uganda
Japan Belgium Algeria
Venezuela Liberia
Kenya
Rica
Pakistan CostaBurkina Republic Armenia Lebanon
Portugal Dominican Faso
MaltaPoland
Italy Brazil Israel
1.5
Chile Mexico
Nicaragua
El Salvador Ecuador
South Korea
Estonia Belarus
Mauritania Russia
Ethiopia
Paraguay Bolivia Hungary
Cameroon
Panama Guatemala
Honduras Mongolia
Argentina TaiwanChad
1
Trinidad & Tobago Peru LatviaLithuania
Moldova
Haiti Ukraine
Togo
.5
0 .1 .2 .3 .4
Country average corruption experiences
Fitted Values
33
Figure 5: Robustness of Main Results to Reverse Causation
(a) Corruption Experiences
0.1
Beta(Gamma)
0.05
0
-0.018 -0.016 -0.014 -0.012 -0.01 -0.008 -0.006 -0.004 -0.002 0
Gamma -0.05
-0.1
-0.15
-0.2
-0.25
Beta(Gamma) Beta(Gamma)-2*se(betaOLS) Beta(Gamma)+2*se(OLS)
(b) Corruption Perceptions
0.1
Beta(Gamma)
0
-0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0
Gamma -0.1
-0.2
-0.3
-0.4
-0.5
-0.6
Beta(Gamma) Beta(Gamma)-2*se(betaOLS) Beta(Gamma)+2*se(OLS)
34
Table 1: Bivariate Cross Country and Fixed Effects Regressions on the Relationship
between Confidence in Institutions and Corruption
(1) (2) (3) (4)
Confidence in Confidence in Confidence in Confidence in
institutions institutions institutions institutions
cross-country fixed effects cross-country fixed effects
Corruption experiences -5.164*** -0.287***
(-3.16) (-8.94)
Corruption perceptions -1.785** -0.854***
(-2.35) (-21.32)
_cons 2.482*** 1.287*** 2.993*** 1.919***
(9.53) (176.37) (5.60) (58.70)
N 103 78063 103 78063
No. of countries 103 103 103 103
R-sq 0.102 0.230 0.059 0.271
t-statistics in parentheses: * p<0.10, ** p<0.05, *** p<0.01
35
Table 2: Fixed Effects Regressions Including Control Variables
(1) (2) (3) (4)
Confidence in Confidence in Confidence in Confidence in
institutions institutions institutions institutions
Corruption experiences -0.298*** -0.282***
(-7.73) (-7.41)
Corruption perceptions -0.870*** -0.865***
(-20.71) (-20.54)
Male 0.00637 -0.00831
(0.30) (-0.41)
Age -0.0167*** -0.0149***
(-6.51) (-5.80)
Age2 0.000210*** 0.000190***
(7.39) (6.69)
Married 0.0933*** 0.0775***
(4.57) (3.76)
Secondary education -0.121*** -0.115***
(-3.94) (-4.02)
Tertiary education -0.0853 -0.108**
(-1.65) (-2.45)
Income -0.000873 -0.0104
(-0.07) (-0.82)
Internet access -0.0437 -0.0600**
(-1.38) (-2.08)
TV -0.0321 -0.0228
(-0.66) (-0.48)
_cons 1.273*** 1.619*** 1.926*** 2.324***
(192.07) (11.91) (56.67) (17.86)
N 57095 57095 57095 57095
No. of countries 94 94 94 94
R-sq 0.226 0.230 0.271 0.275
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
36
Table 3: Fixed Effects Regressions Controlling for Kvetch
(1) (2) (3) (4)
Confidence in Confidence in Confidence in Confidence in
institutions institutions institutions institutions
Corruption experiences -0.280*** -0.185***
(-6.67) (-5.62)
Corruption perceptions -0.873*** -0.518***
(-20.05) (-16.37)
Male 0.0123 -0.00647 -0.00512 -0.0144
(0.52) (-0.34) (-0.23) (-0.78)
Age -0.0157*** -0.00135 -0.0141*** -0.00118
(-5.90) (-0.54) (-5.21) (-0.47)
Age2 0.000198*** 0.0000456* 0.000180*** 0.0000425
(6.76) (1.66) (6.02) (1.53)
Married 0.0890*** 0.0399** 0.0739*** 0.0342*
(4.05) (2.13) (3.37) (1.80)
Secondary education -0.131*** -0.118*** -0.124*** -0.116***
(-3.94) (-4.79) (-4.04) (-4.81)
Tertiary education -0.0822 -0.0791* -0.102** -0.0916**
(-1.49) (-1.74) (-2.18) (-2.21)
Income -0.000267 -0.0470*** -0.00943 -0.0488***
(-0.02) (-3.68) (-0.68) (-3.76)
Internet access -0.0612* -0.0784*** -0.0817** -0.0872***
(-1.71) (-3.17) (-2.55) (-3.68)
TV -0.0327 -0.101*** -0.0234 -0.0943**
(-0.67) (-2.86) (-0.49) (-2.62)
Ladder of life 0.0151*** 0.0139**
(2.74) (2.61)
Standard of living 0.228*** 0.220***
(8.30) (8.02)
Emotions 0.0533*** 0.0519***
(4.90) (4.77)
Economy good/bad 0.530*** 0.489***
(17.52) (17.20)
Economic outlook -0.190*** -0.183***
(-11.61) (-11.70)
Corruption trend -0.272*** -0.207***
(-15.54) (-12.61)
Religious organizations 0.705*** 0.689***
(19.25) (18.81)
_cons 1.537*** 1.752*** 2.238*** 2.034***
(10.26) (12.77) (15.94) (14.40)
N 49019 49019 49019 49019
No. of countries 90 90 90 90
R-sq 0.218 0.378 0.264 0.392
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
37
Table 4: Disaggregation of "Confidence in Institutions" Index
(1) (2) (3) (4)
Military Judiciary National Gov. Elections
linear linear linear linear
Corruption experiences -0.0431*** -0.0576*** -0.0480*** -0.0367***
(-4.74) (-5.02) (-4.70) (-3.48)
_cons 0.345*** 0.358*** 0.617*** 0.432***
(8.82) (6.91) (13.47) (9.41)
R-sq 0.232 0.234 0.278 0.271
(1) (2) (3) (4)
Military Judiciary National Gov. Elections
linear linear linear linear
Corruption perceptions -0.0623*** -0.133*** -0.166*** -0.157***
(-6.82) (-12.18) (-13.54) (-13.99)
_cons 0.379*** 0.430*** 0.707*** 0.518***
(9.48) (8.01) (14.98) (11.22)
R-sq 0.234 0.241 0.291 0.282
N 49019 49019 49019 49019
No. of countries 90 90 90 90
t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01
38
Table 5: Disaggregation into Subgroups Depending on Geographic Region, Level of Corruption, and Income
CORRUPTION EXPERIENCES CORRUPTION PERCEPTIONS
Mean Standard Proportion of Mean Standard Proportion of
No. of estimated deviation of Proportion of negative and estimated deviation of Proportion of negative and
countries slope slope negative signficant slope slope negative signficant
in group coefficient coefficient coefficients coefficients* coefficient coefficient coefficients coefficients*
Full sample 90 -0.134 0.255 0.678 0.222 -0.466 0.376 0.911 0.633
Europe & Central Asia 11 -0.172 0.200 0.727 0.272 -0.312 0.419 0.818 0.454
Middle-East & North Africa 4 -0.153 0.282 0.750 0.250 -0.730 0.250 1.000 1.000
East Asia & Pacific 9 -0.060 0.175 0.667 0.222 -0.224 0.297 0.778 0.333
South Asia 5 -0.319 0.265 0.800 0.600 -0.104 0.259 0.600 0.400
Latin America & Caribbean 19 -0.194 0.271 0.684 0.369 -0.490 0.191 1.000 0.737
Sub-Saharan Africa 18 -0.061 0.278 0.611 0.222 -0.472 0.521 0.889 0.556
High income: OECD 20 -0.113 0.281 0.650 0.000 -0.561 0.211 1.000 0.800
High income: non-OECD 4 -0.094 0.143 0.750 0.000 -1.006 0.385 1.000 0.750
Low level of corruption
30 -0.066 0.241 0.600 0.000 -0.485 0.407 0.933 0.633
experiences/perceptions
Medium level of corruption
30 -0.243 0.238 0.833 0.367 -0.447 0.345 0.867 0.667
experiences/perceptions
High level of corruption
30 -0.093 0.256 0.600 0.300 -0.465 0.384 0.933 0.600
experiences/perceptions
Low income 30 -0.129 0.272 0.600 0.300 -0.370 0.437 0.867 0.400
Medium income 30 -0.153 0.244 0.733 0.300 -0.449 0.406 0.867 0.733
High income 30 -0.121 0.256 0.700 0.067 -0.579 0.234 1.000 0.767
* statistically significant coefficients at at least the 5 percent level were included
39
Table 6: Why Do Adverse Effects of Corruption Matter?
(1) (2) (3) (4) (5) (6)
Achieve change Achieve change Achieve change Achieve change Achieve change Achieve change
by peaceful by peaceful by peaceful by peaceful by peaceful by peaceful
means means means means means means
Confidence in
institutions 0.0958*** 0.0776*** 0.0764*** 0.0767***
(8.70) (6.48) (6.41) (6.61)
Corruption
experiences -0.0833*** -0.0690**
(-2.84) (-2.45)
Corruption
perceptions -0.0572* -0.0174
(-1.76) (-0.58)
_cons -0.410*** -0.349** -0.105 -0.0632 -0.354** -0.337**
(-15.02) (-2.42) (-0.77) (-0.46) (-2.48) (-2.36)
N 46249 46249 46249 46249 46249 46249
No. of countries 89 89 89 89 89 89
Controls no yes yes yes yes yes
(1) (2) (3) (4) (5) (6)
Like to move to Like to move to Like to move to Like to move to Like to move to Like to move to
other country? other country? other country? other country? other country? other country?
Confidence in
institutions -0.127*** -0.0676*** -0.0629*** -0.0622***
(-8.45) (-4.63) (-4.27) (-4.26)
Corruption
experiences 0.249*** 0.236***
(7.15) (6.63)
Corruption
perceptions 0.130*** 0.0964***
(4.35) (3.22)
_cons 0.409*** 0.229 0.0492 -0.0486 0.243 0.163
(23.00) (1.07) (0.23) (-0.22) (1.15) (0.76)
N 34184 34184 34184 34184 34184 34184
No. of countries 69 69 69 69 69 69
Controls no yes yes yes yes yes
(1) (2) (3) (4) (5) (6)
Voiced opinion Voiced opinion Voiced opinion Voiced opinion Voiced opinion Voiced opinion
to official to official to official to official to official to official
Confidence in
institutions 0.0259*** 0.0127 0.0183* 0.0127
(4.25) (1.26) (1.80) (1.25)
Corruption
experiences 0.301*** 0.305***
(9.00) (9.09)
Corruption
perceptions -0.00537 0.00135
(-0.19) (0.05)
_cons -0.820*** -2.003*** -1.925*** -1.960*** -1.981*** -2.004***
(-4.44) (-10.63) (-10.41) (-10.12) (-10.97) (-10.65)
N 48774 48774 48774 48774 48774 48774
No. of countries 90 90 90 90 90 90
Controls no yes yes yes yes yes
t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01
Models with control variables include the complete set of controls
40
Table 7: Variable Descriptions
Variable Wording of Question in GWP Definition
Confidence in institutions Index composed of four subcategories of this question: "In scale of 0 to 4 with 4
this country, do you have confidence in each of the following, indicating highest
or not? How about the military? Judicial system and courts? confidence
National government? Honesty of elections?"
Corruption experiences "Sometimes people have to give a bribe or a present in order dummy: 1 indicating
to solve their problems. In the last 12 months, were you, exposure to bribery
personally, faced with this kind of situation, or not (regardless
of whether you have the bribe/present or not)?"
Corruption perceptions "Is corruption widespread throughout the government in this dummy: 1 indicating
country, or not?" corruption is widespread
Male Share of male respondents
Age Age in years
Married "What is your current marital status?"; responses of dummy: 1 indicating
"married" as well as "domestic partner" were aggregated to married/domestic partner
form the "Married" variable
Secondary education "What is your highest level of education?" dummy: 1 indicating highest
level is tertiary education
Tertiary education "What is your highest level of education?" dummy: 1 indicating highest
level is secondary education
Income "What is your total monthly household income, before taxes? Income in US dollars
Please include income from wages and salaries, remittances
from family member living elsewhere, farming and all other
sources."
Internet access "Does your home have access to the internet?" dummy: 1 indicating yes
TV "Does your home have a television?" dummy: 1 indicating yes
Ladder of life "Imagine a ladder numbered from zero at the bottom to ten at scale of 0 to 10 with 10
the top. Suppose we say that the top of the ladder represents being best life
the best possible life for you, and the bottom of the ladder
represents the worst possible life for you. On which step of
the ladder would you say you personally feel you stand at
this time, assuming that the higher the step the better you
feel about your life, and the lower the step the worse you feel
about it? Which step comes closest to the way you fell?"
Standard of living "Are you satisfied or dissatisfied with your standard of living, (0 or 1) with 1 indicating
all the things you can buy and do?". satisfied
Emotions Index composed of three subcategories of this question: scale of 0 to 3 with 3
"Did you experience the following feelings during a lot of the indicating yes to all 3
day yesterday? How about Worry? Stress? Happiness?" questions
Economy good/bad "Do you believe the current economic conditions in this dummy: 1 indicating good
country are good, or not?"
Economic outlook "Right now, do you think the economic conditions in this dummy: 1 indicating better
country as a whole, are getting better or getting worse?"
Corruption trend "Do you think the level of corruption in this country is lower, dummy: 1 indicating
about the same, or higher than it was 5 years ago?" corruption is higher
Religious organizations "In this country, do you have confidence in each of the dummy: 1 indicating
following, or not? How about religious organizations confidence
(churches, mosques, temples etc.)?"
Voiced opinion to public "Have you done any of the following in the past month? How dummy: 1 indicating "yes"
official about voiced your opinion to a public official?"
Achieve change by "Some people believe that groups that are oppressed and dummy: 1 indicating
peaceful means are suffering from injustice can improve their situations by "peaceful means alone will
peaceful means alone. Other do not believe that peaceful work"
means alone will work to improve the situation for such
oppressed groups. Which do you believe?"
Like to move to other "Ideally, if you had the opportunity, would you like to move dummy: 1 indicating "would
country permanently to another country, or would you prefer to like to move"
continue living in this country?"
41
Table 8: Countries in Core Sample by Geographical Region
Europe & East Asia & Latin America Sub-Saharan Middle-East & High income: High income:
Central Asia Pacific South Asia & Caribbean Africa North Africa OECD non-OECD
Armenia Cambodia Bangladesh Argentina Botswana Algeria Australia Estonia
Azerbaijan Indonesia India Bolivia Burkina Faso Djibouti Austria Israel
Belarus Laos Nepal Brazil Burundi Iran Belgium Malta
Hungary Malaysia Pakistan Chile Cameroon Lebanon Canada Trinidad & Tobago
Latvia Mongolia Sri Lanka Colombia Chad Denmark
Lithuania Philippines Costa Rica Ethiopia Finland
Moldova Taiwan Dominican Rep. Ghana France
Poland Thailand Ecuador Kenya Germany
Russia Vietnam El Salvador Liberia Ireland
Turkey Guatemala Madagascar Italy
Ukraine Haiti Mauritania Japan
Honduras Niger Luxembourg
Mexico Senegal Netherlands
Nicaragua Sierra Leone New Zealand
Panama Tanzania Norway
Paraguay Togo Portugal
Peru Uganda South Korea
Uruguay Zambia Spain
Venezuela Sweden
United Kingdom
42